BEST-WORST METHOD

The First International Workshop on Best-Worst Method (BWM 2020)

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BOOK OF ABSTRACTS 2020

BEST-WORST METHOD A MULTI-CRITERIA DECISION-MAKING METHOD

THE FIRST INTERNATIONAL WORKSHOP ON

BEST-WORST METHOD

11-12 June 2020

Delft, The Netherlands

www.bestworstmethod.com

http://www.bestworstmethod.com/


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SCIENTIFIC COMMITTEE:

Jafar Rezaei (Chair), Delft University of Technology, The Netherlands

Joseph Sarkis, Worcester Polytechnic Institute, USA

Matteo Brunelli, University of Trento, Italy

Negin Salimi, Wageningen University and Research, The Netherlands

Kannan Govindan, Southern Denmark University, Denmark

Majid Mohammadi, Jheronimus Academy of Data Science, The Netherlands

James Liou, National Taipei University of Technology, Taiwan

Himanshu Gupta, Indian Institute of Technology Dhanbad, India

Jingzheng Ren, The Hong Kong Polytechnic University, Hong Kong SAR, China

Simonov Kusi-Sarpong, University of Southampton, United Kingdom

Huchang Liao, Sichuan University, China

ORGANIZING COMMITTEE:

Jafar Rezaei (Chair), Delft University of Technology, The Netherlands

Fuqi Liang, Delft University of Technology, The Netherlands

Ruchika Kalpoe, Delft University of Technology, The Netherlands

Longxiao Li, Delft University of Technology, The Netherlands

CONTACT: [email protected]

https://www.tudelft.nl/tbm/over-de-faculteit/afdelingen/engineering-systems-and-services/people/associate-professors/dr-j-jafar-rezaei/

mailto:[email protected]


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Thursday, June 11th, 2020

08:00-08:15 Welcome and opening | Jafar Rezaei

08:15-09:15 Lecture 1: Foundations of BWM Jafar Rezaei

09:15-09:30 Break

09:30-10:15 Lecture 2: Multiplicative BWM Matteo Brunelli

10-15-10:30 Break

10:30-11:15 Lecture 3: Bayesian BWM Majid Mohammadi

11:15-12:30 Break

Session 1 (presentations) | Chair: Himanshu Gupta

12:30-14:00 A weight determination tool for Food Supply chain

practices

Morteza Yazdani, Ali

Ebadi Torkayesh,

Prasenjit Chatterjee

A novel group multi-criteria decision-making approach

for establishing users’ technology acceptance in the

context of apparel e-commerce

Ruchika Kalpoe, Jafar

Rezaei, Hadi Asghari

Evaluating Strategies for Implementing Industry 4.0:

A Hybrid Expert Oriented Approach of BWM and

Interval Valued Intuitionistic Fuzzy TODIM

Hannan Amoozad

Mahdiraji, Edmundas

Kazimieras Zavadskas,

Marinko Skare, Fatemeh

Zahra Rajabi Kafshgar,

Alireza Arab

14:00-14:30 Break

Session 2 (presentations) | Chair: Jingzheng Ren

14:30-16:00 Prioritizing the broader dimensions of Service Supply

Chain Performance: A Case of Majan Electricity

Company

Haidar Abbas, Sanyo

Moosa

Social sustainable supplier evaluation and selection: A

Group Decision Support Approach

Chunguang Bai, Simonov

Kusi-Sarpong, Hadi Badri

Ahmadi, Joseph Sarkis

Inland terminal location selection: Developing and

applying a consensus model for BWM group decision-

making

Fuqi Liang, Kyle

Verhoeven, Matteo

Brunelli, Jafar Rezaei

PROGRAM


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Friday, June 12th 2020

Session 3 (presentations) | Chair: Matteo Brunelli

08:00-09:30 Best-Worst Method (BWM), its Family and

Applications: Quo Vadis?

Ruojue Lin, Yue Liu,

Jingzheng Ren

A Comparison Between AHP and BWM Models to

Analyze Travel Mode Choice

Sarbast Moslem

A Hybrid Spanning Trees Enumeration and BWM (STE-

BWM) for Decision Making under Uncertainty: An

application in the UK Energy Supply Chain

Amin Vafadarnikjoo

09:30-10:00 Break

Session 4 (presentations) | Chair: Majid Mohammadi

10:00-11:30 Multi-criteria competence analysis (MCCA): A case

study on crowdsourcing delivery personnel on takeaway

platform

Longxiao Li, Xu Wang,

Jafar Rezaei

A hybrid failure assessment approach by an FMEA using

fuzzy Bayesian network and fuzzy best-worst method

Muhammet Gul, Melih

Yucesan, Erkan Celik

Using Bayesian Best Worst Method to Assess the

Airport Resilience

Huai-Wei Lo, James

J.H. Liou, Chun-Nen

Huang

11:30-12:30 Break

Session 5 (presentations) | Chair: Fuqi Liang

12:30-14:00 Identifying and ranking the barriers to organizational

productivity of the railway industry - using the Best-

Worst Method

Mahdie Hamedi,

Mohamad Sadeq

Abolhasani, Hamidreza

Fallah Lajimi, Zahra

Jafari Soruni

Identifying and Prioritizing Competency Factors for

Platforms Managing Service Providers in Knowledge-

Intensive Crowdsourcing Context

Biyu Yang, Xu Wang,

Zhoufei Ding

An analysis of sustainable business practices: An

emerging economy perspective

Himanshu Gupta,

Ashwani Kumar

14:00-14:30 Closing the workshop | Jafar Rezaei


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1ST INTERNATIONAL WORKSHOP ON BEST-WORST METHOD

11-12 June 2020, Delft, The Netherlands Page

Lecture 1: Foundations of Best-Worst Method

Jafar Rezaei

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Lecture 2: The multiplicative Best-Worst Method

Matteo Brunelli

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Lecture 3: The Bayesian Best-Worst Method

Majid Mohammadi

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A weight determination tool for Food Supply chain practices

Morteza Yazdani, Ali Ebadi Torkayesh, Prasenjit Chatterjee

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A novel group multi-criteria decision-making approach for establishing users’ technology

acceptance in the context of apparel e-commerce

Ruchika Kalpoe, Hadi Asghari, Jafar Rezaei

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Evaluating Strategies for Implementing Industry 4.0: A Hybrid Expert Oriented

Approach of BWM and Interval Valued Intuitionistic Fuzzy TODIM

Hannan Amoozad Mahdiraji, Edmundas Kazimieras Zavadskas, Marinko Skare, Fatemeh Zahra

Rajabi Kafshgar, Alireza Arab

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Prioritizing the broader dimensions of Service Supply Chain Performance: A Case of

Majan Electricity Company

Haidar Abbas, Sanyo Moosa

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Social sustainable supplier evaluation and selection: A Group Decision Support Approach

Chunguang Bai, Simonov Kusi-Sarpong, Hadi Badri Ahmadi, Joseph Sarkis

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Inland terminal location selection: Developing and applying a consensus model for

BWM group decision-making

Fuqi Liang, Kyle Verhoeven, Matteo Brunelli, Jafar Rezaei

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Best-Worst Method (BWM), its Family and Applications: Quo Vadis?

Ruojue Lin, Yue Liu, Jingzheng Ren

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A Comparison Between AHP and BWM Models to Analyze Travel Mode Choice

Sarbast Moslem

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A Hybrid Spanning Trees Enumeration and BWM (STE-BWM) for Decision Making

under Uncertainty: An application in the UK Energy Supply Chain

Amin Vafadarnikjoo

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Multi-criteria competence analysis (MCCA): A case study on crowdsourcing delivery

personnel on takeaway platform

Longxiao Li, Xu Wang, Jafar Rezaei

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A hybrid failure assessment approach by an FMEA using fuzzy Bayesian network and

fuzzy best-worst method

Muhammet Gul, Melih Yucesan, Erkan Celik

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Using Bayesian Best Worst Method to Assess the Airport Resilience

Huai-Wei Lo, James J.H. Liou, Chun-Nen Huang

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Identifying and ranking the barriers to organizational productivity of the railway

industry - using the Best-Worst Method

Mahdie Hamedi, Mohamad Sadeq Abolhasani, Hamidreza Fallah Lajimi, Zahra Jafari Soruni

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Identifying and Prioritizing Competency Factors for Platforms Managing Service

Providers in Knowledge-Intensive Crowdsourcing Context

Biyu Yang, Xu Wang, Zhoufei Ding

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An analysis of sustainable business practices: An emerging economy perspective

Himanshu Gupta, Ashwani Kumar

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Lecture 1: Foundations of Best-Worst Method

Jafar Rezaei1

1 Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands

(E-mail: [email protected])

In this lecture, we first discuss the philosophy behind the best-worst method (BWM). Then, we

will discuss how an MCDM problem can be formulated and solved by BWM. More specifically

the non-linear and linear models of BWM are discussed with some examples. We then discuss

the way we can check the consistency and concentration of the findings (weights of the criteria

or overall value of alternatives).

We will also discuss some salient features of the method and explain some practical

considerations when using the method.

The lecture is designed mainly for those with limited knowledge about the BWM. However,

we will also discuss some topics which are not discussed in the existing literature.

Lecture 2: The multiplicative Best-Worst Method

Matteo Brunelli1

1Department of Industrial Engineering, University of Trento, Italy


In this presentation, we shall consider the best-worst method from a more algebraic point of

view and inquiry into the metric used to find the weights. By means of abstract algebra, we

shall consider and justify an alternate metric. In particular, we will see that this new metric can

lead to a simple optimization problem and is supported by a more general concept of distance.

In fact, albeit seemingly more complex the new optimization problem (i) can be equivalently

formulated as a linear optimization problem, and (ii) is a special case of the notion of distance

for continuous Abelian linearly ordered groups.

While having these attractive features, the new (multiplicative) formulation of the best-worst

method retains the characteristics that made the original best-worst method appealing: the logic

of using the best and the worst criteria as pivots for the comparisons, the minimization of the

maximum discrepancy, the ability of providing an intrinsic measure of inconsistency, the

possibility of estimating interval-valued weights.

The presentation will be self-contained and no preliminary notion of abstract algebra is

necessary and it hopefully will raise some questions and sparkle a discussion, besides showing

a variant of the best-worst method.





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Lecture 3: The Bayesian Best-Worst Method

Majid Mohammadi1

1Jheronimus Academy of Data Science, The Netherlands


In this presentation, a probabilistic extension of the best-worst method (BWM) is presented,

where the inputs and the outputs of the original method are modeled by using probability

distributions. The new modeling, though seemingly different, would preserve the underlying

ideas of the original best-worst method. As such, the problem of identifying the weights of

criteria in the BWM is translated into a statistical inference problem, and a Bayesian model is

especially-tailored accordingly. We further introduce a new ranking scheme for decision

criteria, called credal ranking, where a confidence level is assigned to measure the extent to

which a group of DMs prefers one criterion over one another.

The presentation is primarily focused on the group decision-making problem within the

framework of the BWM, but other types of decision-making problems are discussed. Also,

different ways for extending the current model are put forward for further discussions.


The First International Workshop on Best-Worst Method (BWM 2020) Session 1

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A weight determination tool for Food Supply chain practices

Morteza Yazdani1, Ali Ebadi Torkayesh2, Prasenjit Chatterjee3

1Universidad Loyola Andalucia, Seville, Spain ([email protected])

2Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

([email protected])

3MCKV Institute of Engineering, West Bengal, India ([email protected])

Keywords: Food supply chain, weighting tools, Best-Worst Method, Supply chain practices,

MCDM

Abstract

Food supply chain (FSC) is one of the globally important and critical supply chain networks

which is designed for perishable edible products. It is defined as series of operations from

production farms to manufacturers to distribution centres that deliver agricultural products to

the final consumers. Identification of food supply chain practices (FSCP) is an important

process where decision makers should select most effective technological, economic,

environmental, and social factors that contribute to FSC management. Unlike other applications

of supply chain management, FSC is always under surveillance of different environmental,

social and economic circumstances. FSC management and its corresponding operations should

be deliberately addressed in order to maximize the satisfaction of final consumers and profit of

food companies. However, determination of importance of each factor is a complicated task

where decision makers can become unable to do so based on the biasedness of their decisions.

Multi Criteria Decision Making models provide decision makers with reliable weight

determination methods in order to obtain the optimal weight of each factors. Best-Worst

Method (BWM) is one of the promising Multiple Criteria Decision Making models that is

frequently used to determine weight of decision factors for MCDM problems. A hierarchical

weight determination is developed based on BWM model. The proposed model can be applied

to assign the relevant weights for MCDM problems such as food logistic provider selection,

and so on.





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A novel group multi-criteria decision-making approach for establishing

users’ technology acceptance in the context of apparel e-commerce

Ruchika Kalpoe*1, Hadi Asghari 2, Jafar Rezaei 3

1 Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands

(E-mail: [email protected]) 3 Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands

(E-mail: [email protected]) 2 Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands


Keywords: Returns management, Apparel e-commerce, Customer-based information

technologies, Multi-Criteria Decision-Making, Bayesian Best-Worst Method

1.Introduction

Although e-commerce has its benefits, it also imposes societal implications. With the

increase of online purchases, the number of order returns also increases (Minnema, Bijmolt,

& Gensler, 2017). According to Minnema et al. (2017), approximately 30% of all online

purchases in the Netherlands are returned to the sender, which imposes structural problems

for online retailers. Of all returned products, apparel is the biggest part. According to Wiese,

Toporowski, & Zielke (2012), returns for apparel items are more common than for most

other products, due to the many apparel attributes. Of all the returned products bought

online, 40% are apparel items (Edwards, McKinnon, & Cullinane, 2010). For apparel e-

commerce retailers, the increase of apparel returns has implications such as extra quality

checks, extra administrative work, re-packaging and storing of apparel, which furthermore

results in an increase of logistic costs (Kennisinstituut voor Mobiliteitsbeleid, 2017). Due

to the increase in order returns, the number of transport van-movements in residential areas

has also increased, which imposes consequences for the air quality, traffic safety, the overall

living environment of cities and the congestion problem the Netherlands is currently

confronted with (Kennisinstituut voor Mobiliteitsbeleid, 2017).

Whilst most research so far has been conducted about monetary instruments and efficient

transport routing and handling of returns of online purchased apparel items, not much

empirical research is conducted so far about customer-based instruments that can be used

during the customers’ online screening process of apparel, in order to prevent unnecessary

apparel returns. Consequently, so far empirical studies which 1) examine/compare the

perceived effectiveness of various customer-based technological concepts in addressing

online purchased apparel return reasons and 2) assess the users’ technology preference, are

sparse. Therefore, this research aims to establish what the customers’ preference is

regarding technological alternatives which can be used during the online screening process

of apparel items, in order to increase customers’ online apparel purchase successes and

reduce unnecessary returns. Since the technologies are designed to be used by customers,

its success relies greatly on the customers usage. Therefore, the research is mainly

approached from the users (customers) perspective.

2. Method and Data

In order to eventually understand the users preference of technologies, the Technology

Acceptance Model (TAM) was used, developed by Davis (Davis, 1986). In literature, TAM

is mostly operationalized using Structural Equation Modelling (SEM), which requires a





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large sample size to produce reliable results. Since due to time and budget constraints it was

not viable to acquire a large sample size, a less data extensive, simpler and reliable approach

to predict the customers’ acceptance regarding various technological alternatives was

needed. As a result, a Multi-Criteria Decision-Analysis (MCDA) approach is applied,

wherein the novel Bayesian BWM developed by Mohammadi & Rezaei (2019) is applied

to operationalize TAM. This approach involved identifying various indicators, quantifying

the importance of each indicator through the assigned preference and determining which

indicator has the highest impact on technology acceptance through the assigned weight. The

influence on technology acceptance is quantified through the computed weights of each

indicator (i.e. criteria). Criteria with high optimal group weights are considered to have a

significant impact on technology acceptance, suggesting that a high level of users’

(customers’) acceptance can be realized when scoring well on each criterion.

Following the MCDA approach, first a set of alternatives needed to be established. For this,

a literature study was conducted through which various apparel return reasons were

established, followed by various customers-based instruments. Based on this, the required

apparel attribute information customers need to have upfront were identified and

technological alternatives were composed. Afterwards, a set of decision-criteria used to

evaluate the technological alternatives was established through a thorough literate study

regarding TAM. The set was finalized with the opinion of online apparel experts. Through

an online BWM survey, the users’ (online apparel shoppers’) optimal group weights per

criterion was acquired. The scores of each technological concept was acquired through six

apparel e-commerce expert interviews stemming from four apparel e-commerce retailers in

the Netherlands. To obtain the scores per technological alternative with respect to each

criterion, the Bayesian BWM was again applied. As a result, the interview was constructed

using the imposed structure of the BWM.

3. Results and main conclusion

The results have shown that predicting the technology acceptance by operationalizing TAM

can be done using the aforementioned MCDA approach as well. The novel Bayesian BWM,

developed by Mohammadi & Rezaei (2019), is applied to a real-life problem (apparel e-

commerce) to check its robustness. The result show that the technological alternative which

has the highest probability of achieving customers’ acceptance is also the one which is

currently the most employed by online apparel retailers in the Netherlands. This shows that

the novel Bayesian BWM method is indeed a successful method which can predict

technology acceptance and preference.

All the results will be presented in the conference.

References

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user

information systems: theory and results (MIT). Retrieved from

http://hdl.handle.net/1721.1/15192

Edwards, J. B., McKinnon, A. C., & Cullinane, S. L. (2010). Comparative analysis of the carbon

footprints of conventional and online retailing: A “last mile” perspective. International

Journal of Physical Distribution and Logistics Management, 40(1–2), 103–123.

Kennisinstituut voor Mobiliteitsbeleid. (2017). Stedelijke distributie en gedrag. Retrieved from

https://www.kimnet.nl/binaries/kimnet/documenten/rapporten/2017/06/06/gedrag-en-

stedelijke-distributie/Stedelijke+distributie+en+gedrag.pdf.


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Minnema, A, Bijmolt, T.H., Gensler, S. (2017). 3. Oorzaken en gevolgen van het terugsturen

van online aankopen. In Ontwikkelingen in het martktonderzoek: Jaarboek Markt

Onderzoek Associatie (42nd ed.). Retrieved from http://moa04.artoo.nl/clou-moaweb

images/images/bestanden/pdf/Jaarboeken_MOA/MOA_JAARBOEK_2017_HFST3.pdf

Mohammadi, M., & Rezaei, J. (2019). Bayesian best-worst method: A probabilistic group

decision making model. Omega, 102075.

Wiese, A., Toporowski, W., & Zielke, S. (2012). Transport-related CO2 effects of online and

brick-and-mortar shopping: A comparison and sensitivity analysis of clothing retailing.

Transportation Research Part D: Transport and Environment, 17(6), 473–477.


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Evaluating Strategies for Implementing Industry 4.0: A Hybrid Expert

Oriented Approach of BWM and Interval Valued Intuitionistic Fuzzy

TODIM

Hannan Amoozad Mahdiraji *1, Edmundas Kazimieras Zavadskas 2, Marinko

Skare3, Fatemeh Zahra Rajabi Kafshgar4, Alireza Arab5

1 Department of Industrial Management, University of Tehran, Tehran, Iran; School of Strategy and

Leadership, Faculty of Business and Law, Coventry University, Coventry, United Kingdom

([email protected]); 2Institute of Sustainable Construction, Gediminas Technikos University, Vilnius, Lithuania Vilnius

(E-mail: [email protected]); 3Economics and Tourism, Juraj Dobrila University of Pula, Preradoviceva, Croatia

(E-mail: [email protected]); 4Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran

(E-mail: [email protected]); 5Faculty of Management, University of Tehran, Tehran, Iran

(E-mail: [email protected]).

Keywords: Industry 4.0; BWM; IVIF; Multi-Criteria Decision Making; TODIM; Information

Systems.

Abstract

Developing and accepting industry 4.0 influences the industry structure and customer

willingness. To a successful transition to industry 4.0, implementation strategies should be

selected with a systematic and comprehensive view to responding to the changes flexibly.

This research aims to identify and prioritize the strategies for implementing industry 4.0.

For this purpose, at first, evaluation attributes of strategies and also strategies to put industry

4.0 in practice are recognized. Then, the attributes are weighted to the experts' opinion by

using the Best Worst Method (BWM). Subsequently, the strategies for implementing

industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the

Interval-Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated

that the attributes of "Technology", "Quality", and "Operation" have respectively the

highest importance. Furthermore, the strategies for "new business models development",

"Improving information systems" and "Human resource management" received a higher

rank. Eventually, some research and executive recommendations are provided. Having

strategies for implementing industry 4.0 is a very important solution. Accordingly, MCDM

methods are a useful tool for adopting and selecting appropriate strategies. In this research,

a novel and Hybrid combination of BWM-TODIM is presented under IVIF information.

Abstract review

Developing in information and communication technology leads to form new facts in many

fields such as manufacturing, resulting in a new concept as the 4th industrial revolution

(intelligent manufacturing and continuous manufactory). Developing and accepting

industry 4.0 influences the industry structure and customer willingness. countries that

implement the Industry 4.0 applications effectively can improve competitive advantages,

labor market, and operational processes. These developments in manufacturing will lead to

an increase in economic growth as well as European commission reported in 2017 about

Key lessons from national industry 4.0 policy initiatives in Europe.




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To a successful transition to industry 4.0, implementation strategies should be selected with

a systematic and comprehensive view to responding to the changes flexibly. Because in the

real world, organizations and companies face limited resources, including financial, human,

technological, and so on. if they want to get into the Implementing Industry 4.0 without a

strategy, they will fail. Therefore, the purpose of the present study is to identify and

prioritize strategies for implementing industry 4.0, thus this research enabling companies to

move further in this direction by focusing more on the specific conditions governing their

proprietary business environment. In this regard, the objectives of the present study are to

identify the attributes for evaluating strategies for implementing industry 4.0, weighting and

determining the relative importance of these attributes, identifying strategies for

implementing industry 4.0, prioritizing these strategies according to the identified attributes

and finally introducing the most optimal ones. Accordingly, MCDM methods are a useful

tool for adopting and selecting appropriate strategies. In this research, a novel and Hybrid

combination of BWM-TODIM is presented under IVIF information.

For this purpose, at first, evaluation attributes of strategies and also strategies to put industry

4.0 in practice are recognized. Six strategies “Human resource management”, “Improving

information systems”, “Work organization and design-oriented”, “Resources and

standardization related”, “New business models development”, “Operation optimization”,

recognized in the literature.

Then, the attributes for evaluating these strategies extracted from literature as “Leadership”,

“Customer”, “Product”, “Operation”, “Culture”, “Staffs”, “Technology”, “Organization”,

“Quality”. Then this attribute weighted by using the Best Worst Method (BWM).

Subsequently, the strategies for implementing industry 4.0 in Iranian auto part manufacture

Company, as a case study, have been ranked based on the Interval-Valued Intuitionistic

Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of

"Technology", "Quality", and "Operation" have respectively the highest importance.

Furthermore, the strategies for "new business models development", "Improving

information systems" and "Human resource management" received a higher rank.

Eventually, some research and executive recommendations are provided. Having strategies

for implementing industry 4.0 is a very important solution. Accordingly, MCDM methods

are a useful tool for adopting and selecting appropriate strategies. In this research, a novel

and Hybrid combination of BWM-TODIM is presented under IVIF information.

The advantages of the BWM, which convinced the authors to use it, are:

• It is compatible with many other existing MCDM methods.

• It can be applied to different MCDM problems with qualitative and quantitative

criteria.

• It is proper for group decision-making.

• It leads to more consistent comparisons, hence more reliable weights/rankings.

• It makes the comparisons in a structured way.

• It is an easy-to-understand and easy-to-apply method.

• It has the debiasing strategy “consider-the-opposite”.

Finally, based on my experience in publishing various domestic and international papers

using BWM method, as well as my research field, which is multi-criteria decision making,

I came to the conclusion and can say that in most research works after finishing work and

obtaining feedback from decision makers, this method reflects their views exactly, and this

satisfaction with the results showed the high effectiveness of this method, which along with

its high efficiency, which was mentioned in the advantages section of this method, makes


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this method one of the most widely used and most cited MCDM weighting method.

Certainly, Dr. Rezaei's efforts have opened a new chapter in this field to all the researchers.

Thank you for your efforts, Dr. Rezaei.

References

Mahdiraji, H. A., Zavadskas, E. K., Skare, M., Kafshgar, F. Z. R., & Arab, A. (2020).

Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of

BWM and interval valued intuitionistic fuzzy TODIM. Economic Research-Ekonomska

Istraživanja, 33(1), 1600-1620.


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Prioritizing the broader dimensions of Service Supply Chain

Performance: A Case of Majan Electricity Company

Haidar Abbas1, Sanyo Moosa2

1Assistant Professor, Department of Business Administration, College of Applied Sciences, Salalah,

Sultanate of Oman

(E-mail: [email protected]) 2Head, Department of Business Administration, College of Applied Sciences, Salalah, Sultanate of

Oman

1. Introduction and Review of Previous Studies

Most of the research attempts in supply chain discipline are made around the manufacturing

(physical goods) supply chains, leaving the service sector scarcely attended till 1990s

(Sengupta, Heiser and Cook, 2006; Zhou, Park, and Yi, 2009; Zhang & Chen, 2015). Ellram,

Tate, and Billington (2004) defined a service supply chain (SSC) as “an integrated management

of service information, service processes, service capacity, service performance and service

funds from the earliest suppliers to the ultimate customers”.

For sustenance as well as excellence, the performance measurement system matters for service

supply chains as much as for the manufacturing supply chains. Among many, the Supply Chain

Operations Reference (SCOR) model (plan, source, make, deliver and return) and the Balance

Score Card (financial, customer, internal business process and innovation and learning) are two

frequently used approaches (Taticchi, Tonelli, and Cagnazzo, 2010). Cho, Lee, Ahn, & Hwang,

(2012) considered service supply chain operations (responsiveness, flexibility and reliability),

customer service (tangibles, assurance and empathy) and corporate management (profitability,

cost, and asset and resource utilization) with a total of twenty-nine (29) sub-parameters while

proposing their performance measurement model for hoteling sector.

This research aims to prioritize performance parameters of the service supply chain at Majan

Electricity Company (MJEC). For the purpose of bringing a more substantiated and

comparative outcomes, it uses the Analytical Hierarchy Process (AHP) and the Best-Worst

Method (BWM). Majan Electricity Company (MJEC) is a closely held Omani Joint Stock

company which was registered under the Commercial Companies Law of Oman. It began its

operations on May 1st, 2005. It bears a license issued by the Authority for Electricity Regulation,

Oman to deal in the regulated distribution and supply of electricity in the North Batinah

Governorate, Al Dhahirah Governorate and the Buraimi Governorate of the Sultanate of Oman.

A SLR of the performance management for humanitarian supply chains (Abidi,, de Leeuw, &

Klumpp, 2014), an analytical framework for the sustainability performance of supply chains

management (Schaltegger & Burritt, 2014), critical determinants of the supply chain

performance (Ab Talib, Hamid, & Thoo, 2015), performance measures related to supply chain

and knowledge management (Ramish & Aslam, 2016), and performance measurement for

reverse supply chains (Butzer, Schötz, Petroschke, & Steinhilper, 2017) are some recent and

relevant studies.

2. Objectives & Research Methodology

The researchers aimed to prioritize the selected measures of service supply chain performance

(satisfaction, empathy, reliability, profitability, responsiveness and efficiency) in the context of

Majan Electricity Company. The researchers have used the Analytical Hierarchy Process (AHP)



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(Saaty and Kirti, 2008) and the Best-Worst Method (BWM) (Rezaei, 2015) to accomplish the

study objectives. The structured questionnaire meant for Analytical Hierarchy Process (AHP)

was administered on a total of eight (08) respondents whereas the other questionnaire for the

Best-Worst Method (BWM) was administered on a number of six (06) respondents holding

some managerial positions in their respective branches.

3. Results and Discussion

The performance dimensions are listed in order of their reported importance by the two different

groups of respondents which were analyzed using different methods.

4.1) Analytical Hierarchy Process (AHP): satisfaction, profitability, responsiveness, efficiency,

empathy, and reliability.

4.2) Best-Worst Method (BWM): profitability, satisfaction, responsiveness, efficiency,

empathy, and reliability.

All the results will be presented in the conference.

4. Limitations and directions for the future research

Given the limited number of respondents and a single entity & sector focussed study, the future

researchers may take a larger sample as well as conduct a comparative study by taking one

service supply chain(s) and one or more manufacturing supply chain(s).

Note: This research paper was submitted to a journal which expressed certain reservations. The

authors withdrew it and developed it in the light of the inputs. The authors expect to learn and

incorporate certain latest developments in this method, if recommended by the conference

session chair and the peer researchers.

Acknowledgement: The authors are grateful to one of their students, Ms. Khadija Al-

Maktoumi (Majan Electricity Company) for the support extended by her in the data collection

phase. We are equally grateful to our respondents who spared time to provide their valuable

inputs.

References

Ab Talib, M. S., Abdul Hamid, A. B., & Thoo, A. C. (2015). Critical success factors of supply

chain management: a literature survey and Pareto analysis. EuroMed Journal of

Business, 10(2), 234-263.

Abidi, H., de Leeuw, S., & Klumpp, M. (2014). Humanitarian supply chain performance

management: a systematic literature review. Supply Chain Management: An International

Journal, 19(5/6), 592-608.

Butzer, S., Schötz, S., Petroschke, M., & Steinhilper, R. (2017). Development of a performance

measurement system for international reverse supply chains. Procedia Cirp, 61, 251-256.

Cho, D. W., Lee, Y. H., Ahn, S. H., & Hwang, M. K. (2012). A framework for measuring the

performance of service supply chain management. Computers & Industrial

Engineering, 62(3), 801-818.

Ellram, L. M, Tate, W.L., and C. Billington (2004). Understanding and Managing the Services

Supply Chain. Journal of Supply Chain Management, 40(4), 17 – 32.

Ming Zhou, Taeho Park, and John Yi (2009). Commonalities and differences between service

and manufacturing supply chains: Combining operations management studies with supply

chain management. California Journal of Operations Management, 136-143.


18

Dos Santos, T. F., & Leite, M. S. A. (2016). Performance measurement system in supply chain

management: application in the service sector. International Journal of Services and

Operations Management, 23(3), 298-315.

Ramish, A., & Aslam, H. (2016). Measuring supply chain knowledge management (SCKM)

performance based on double/triple loop learning principle. International Journal of

Productivity and Performance Management, 65(5), 704-722.

Rezaei, J., (2015). Best-Worst Multi-Criteria Decision-Making Method, Omega, 53, 49-57.

Saaty, T.L. and Kirti, P. (2008). Group Decision Making: Drawing out and Reconciling

Differences. RWS Publications, Pittsburgh, PA.

Schaltegger, S., & Burritt, R. (2014). Measuring and managing sustainability performance of

supply chains: Review and sustainability supply chain management framework. Supply

Chain Management: An International Journal, 19(3), 232-241.

Sengupta, K., D. R. Heiser, and L. S. Cook (2006). Manufacturing and Service Supply Chain

Performance: A Comparative Analysis. Journal of Supply Chain Management, 42(4), 4-

15.

Taticchi, P., Tonelli, F., and Cagnazzo, L. (2010). Performance measurement and management:

a literature review and a research agenda. Measuring Business Excellence, 14, (1), 4-18.

Zhang, R.Q. and Chen, H.Q. (2015). A Review of Service Supply Chain and Future Prospects.

Journal of Service Science and Management, 8, 485-495.


19

Social sustainable supplier evaluation and selection: A Group Decision

Support Approach

Chunguang Bai1, Simonov Kusi-Sarpong2, Hadi Badri Ahmadi*3, Joseph Sarkis4

1 School of Management and Economics, University of Electronic Science and Technology of China,

Chengdu, China

(E-mail: [email protected]) 2 Portsmouth Business School, University of Portsmouth, Portsmouth, UK

(E-mail: [email protected]) 3 School of Management Science and Engineering, Dalian University of Technology

(E-mail: [email protected]) 4 Foisie Business School, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609-

2280, USA

(Email: [email protected])

Keywords: Sustainability; social sustainability; sustainable supply chains; BWM; TODIM

Abstract

Organizational and managerial decisions are becoming influenced corporate sustainability

pressures. Organizations need to consider economic, environmental, and social dimensions of

sustainability in their decisions if their goal is to become sustainable. Supply chain decisions

play a distinct and critical role in overall sustainability of organizational good and service

outputs. Regulatory demands, stakeholder awareness and increasing pressures, have forced the

hand of organizations to take into consideration sustainability in their decisions. These

suppliers’ serious social consequences range from strike actions due to poor work health and

safety reasons, to employee rights related to poor employment practices. resulting in production

losses and the inability to meet buying firms’ deadlines. Since suppliers provide raw materials,

services, and finished products as inputs to organizational supply chains, their activities play a

critical role in helping firms achieve sustainable and collaborative competitive edge and

increasing performance. A few studies have recently attempted to focus and utilize the social

sustainability dimension separately or in combination with environmental and economic

dimensions in the supplier selection process. To address these issues, this work adopts and

integrates a previously proposed social sustainability attribute framework into the supplier

selection decision problem, with a hybrid of two complementary tools, BWM and TODIM

methodologies under a grey number environment. The specific objectives of this work are as

follows: 1. Introduce a multiple attribute approach that integrates the “Best Worst Method”

(BWM) and TODIM in a grey number environment for the supplier selection decision; 2. To

investigate a multiple attribute social sustainable supplier evaluation and selection process from

a manufacturing sector context; 3. Provide insights in the application of this model to an

emerging economy context (Iran). This study makes the following academic and managerial

contributions: (1) identifies and introduces a proposed social sustainability attributes

framework for guiding general social sustainability decision making; (2) Introduces and applies

a multi- criteria decision-making (MCDM) model that integrates interval grey number based

BWM and TODIM. These analytical tools provide complementary avenues to rank or select

preferred socially sustainable suppliers using expert judgments. In order to directly obtain

relative weights, BWM has been reformulated, a modelling contribution; (3) BWM and interval

grey number are jointly used to overcome the limitations of the TODIM method to solve the

MCDM problem under experts’ uncertain judgments. The interval grey number is more

appropriate to model decision maker judgments extending BWM and TODIM methods to

effectively deal with decision making problems under uncertain and grey environments. Grey-




20

BWM is used to develop the relative weight of attributes to overcome TODIM method

limitations that require additional information about the variable weights.

Methods and Data

To advance the field methodologically, this work introduces the Grey-BWM and Grey-TODIM

methodology to evaluate and select the best social sustainable supplier based on decision-maker

opinions and behavioral characteristics. Interval grey number is applied to numerically model

decision makers’ judgments within the BWM and TODIM methods. Grey-BWM complements

the TODIM method by identifying the social sustainability attribute relative weights. These

combined capabilities make the methodology more realistic and flexible. The biggest Iranian

multinational automobile manufacturing company, employing more than 10% of the

automotive workforce, in the sector intends to take a leading step in improving its social

sustainability performance by selecting a socially conscious supplier for parts. 5 suppliers were

shortlisted by the management. A ten-member (10) team of decision-makers (managers) that

influence the supplier selection decision was involved in the selection process. This team

included a supply manager, assistant supply chain manager, purchasing manager, finance

manager, research and development manager, IT manager, production manager, general

manager, logistics manager and maintenance manager.

Results and Conclusion

In this study, we utilized a novel integrated MCDM tool composed of grey numbers, BWM and

TODIM to investigate social sustainability supplier evaluation and selection. Overall, this work

introduced a comprehensive framework for investigating and supporting social sustainability

supplier evaluation and selection. The framework consists of eight social sustainability

attributes including: ‘Work health and safety’ (WSLH/SSA1); ‘Training education and

community influence’ (TECI/SSA2); ‘Contractual stakeholders’ influence’ (CSI/SSA3);

‘Occupational health and safety management system’ (OHSMS/SSA4); ‘The interests and

rights of employees’ (IRE/SSA5); ‘The rights of stakeholders’ (RS/SSA6); ‘Information

disclosure’ (ID/SSA7); and ‘Employment practices’ (EP/SSA8). The social sustainability

framework was then applied to an Iranian manufacturing company with inputs from ten of their

industrial experts using a novel decision support tool that integrates for the first time grey

system theory, BWM and TODIM approaches for assessing and ranking five suppliers in terms

of their social sustainability performance.

References:

Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of

supply chains using Best Worst Method. Resources, Conservation and Recycling, 126,

99-106.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a

linear model. Omega, 64, 126-130.


21

Inland terminal location selection: Developing and applying a consensus

model for BWM group decision-making

Fuqi Liang*1, Kyle Verhoeven2, Matteo Brunelli3, Jafar Rezaei4

1, 2, 4 Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The

Netherlands

(E-mail: 1, [email protected], 2, [email protected], 4, [email protected]) 3 Department of Industrial Engineering, University of Trento, Trento, Italy


Keywords: Inland terminal location selection; Shipping line; Group BWM; Consensus

Abstract

The purpose of this paper is to develop an inland terminal location selection methodology,

viewed from the perspective of the shipping line designing the inland transport chain, while

also taking into account the objectives of the terminal operator and terminal user

stakeholders. To that end, we develop a consensus model for a group Best-Worst Method

(BWM) to aggregate the evaluations of the various stakeholders. Firstly, potential

alternatives and a group of relative stakeholders are identified by the shipping line, after

which each stakeholder evaluates the location selection problem and identifies its own set

of criteria. Next, BWM is used to prioritize the importance of the criteria identified by the

various stakeholders, and alternatives are evaluated based on the different sets of criteria,

in which the value of each alternative is obtained based on an additive value function for

each stakeholder. Using the proposed consensus model makes it possible to identify the

aggregated values of the alternatives and then selected the desired location. The proposed

method is applied to a real-life case study involving shipping line Maersk, which considered

six locations and nine experts representing three different types of stakeholders. After data

collection and calculation, container volume potential is identified as one of the most

important criteria. Using a sensitivity analysis, we find that a varying influx of container

volume has no impact on the most desirable location.



22

Best-Worst Method (BWM), its Family and Applications: Quo Vadis?

Ruojue Lin1, Yue Liu1, Jingzheng Ren*1

1 Department of Industrial and Systems Engineering, Faculty of Engineering, The Hong Kong

Polytechnic University, Hong Kong Special Administrative Region, China.

(E-mail: [email protected]; [email protected];

[email protected])

Keywords: Best-Worst Method; multi-criteria decision making; interval number; group

decision-making

Abstract

Best-worst method (BWM) is a new efficient multi-criteria decision-making (MCDM) tool

developed by Razeai (2015). Compared with the classical MCDM methods (especially the

Analytic Hierarchy Process), BWM provides more consistent weighting results based on

only two vectors of pairwise comparisons, and it requires less times of comparisons and has

relatively higher consistency. With the features of high efficiency and accuracy, BWM has

been widely applied in various disciplines for solving different types of decision-making

problems. This study aims to have a comprehensive literature review on the applications of

BWM through bibliometric analysis; subsequently investigate the BWM family; then

predict the future research trend of BWM; finally, we present and compare the fuzzy BWM

and the interval BWM.

Specially, firstly, the applications of BWM in different fields, such as energy supply (van

de Kaa et al., 2017; Wan Ahmad et al., 2017), supply chain (Palanisamy et al., 2020), and

transportation (Shojaei et al., 2018), are reviewed, and the bibliometric analysis has been

carried out. Secondly, the extended BWM models are investigated. The BWM has been

improved and combined with fuzzy sets (Moslem et al., 2020), interval numbers

(Hafezalkotob et al., 2020), rough-fuzzy sets (Chen et al., 2020), and other mathematical

theories in order to solve more complex decision-making problems. Thirdly, the potential

development directions of BWM in the future are analyzed according to current research

trends. For example, the group decision-making and the combination of BWM and artificial

intelligence (AI) could be considered as research topics in the future. Finally, the procedures

of fuzzy BWM and interval BWM have been specified and illustrated. In order to promote

the development of BWM and its modified versions, we proposed some effective methods

such as establishing a journal for BWM and developing a convenient computing software

for BWM.

Acknowledgment: The authors would like to express appreciation for the support of the grant

from the Research Committee of The Hong Kong Polytechnic University under student account

code RK22.

References:

Chen, Z., Ming, X., Zhou, T., Chang, Y., & Sun, Z. (2020). A hybrid framework integrating

rough-fuzzy best-worst method to identify and evaluate user activity-oriented service

requirement for smart product service system. Journal of Cleaner Production, 119954.




23

Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). Interval MULTIMOORA

method integrating interval borda rule and interval best-worst-method-based weighting

model: Case study on hybrid vehicle engine selection. IEEE transactions on cybernetics.

Moslem, S., Gul, M., Farooq, D., Celik, E., Ghorbanzadeh, O., & Blaschke, T. (2020). An

integrated approach of best-worst method (bwm) and triangular fuzzy sets for evaluating

driver behavior factors related to road safety. Mathematics, 8(3), 414.

Palanisamy, M., Pugalendhi, A., & Ranganathan, R. (2020). Selection of suitable additive

manufacturing machine and materials through best–worst method (BWM). The

International Journal of Advanced Manufacturing Technology, 1-18.


Shojaei, P., Haeri, S. A. S., & Mohammadi, S. (2018). Airports evaluation and ranking model

using Taguchi loss function, best-worst method and VIKOR technique. Journal of Air

Transport Management, 68, 4-13.

van de Kaa, G., Kamp, L., & Rezaei, J. (2017). Selection of biomass thermochemical

conversion technology in the Netherlands: A best worst method approach. Journal of

Cleaner Production, 166, 32-39.

Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the

external forces affecting the sustainability of oil and gas supply chain using Best Worst

Method. Journal of Cleaner Production, 153, 242-252.


24

A Comparison Between AHP and BWM Models to Analyze Travel Mode

Choice

Sarbast Moslem*1

1Department of Transport Technology and Economics, Faculty of Transportation Engineering and

Vehicle Engineering, Budapest University of Technology and Economics (BME), Hungary


Keywords: Travel mode choice; Analytic Hierarchy Process (AHP); Best-Worst Method

(BWM); Pairwise comparison

Abstract

Evaluating commuting trip patterns plays essential role in urban planning and improvement.

Passenger travels cover the majority of all travels in the urban transportation system and the

mode choice in these type of travels makes severe impact on the sustainability of system

operations. In this work, we endeavor to extend the methodological family of direct mode

choice determination and forecast. The objective is deriving their attitude by stated

comparisons of travel modes. For this objective, two well-proven and widely applied

techniques: the Analytic Hierarchy Process (AHP) and the Best-Worst Method (BWM) have

been selected. Compared to AHP, the Best-Worst Method has significant practical

advantages in user surveys; it needs less time and effort to complete the questionnaire, the

responses are generally more consistent and the response rate is much higher than in an

AHP survey. Two passenger surveys conducted in a Turkish big city, Mersin in 2020. The

conducted results indicate that Public Transport is the most used mobility type. BWM

survey was easier and shorter than AHP survey, moreover, the final scores derived from

BWM are highly reliable as it generates more consistent comparisons compared to AHP.


25

A Hybrid Spanning Trees Enumeration and BWM (STE-BWM) for

Decision Making under Uncertainty: An application in the UK Energy

Supply Chain

Amin Vafadarnikjoo1

1Research Associate in Operations and Supply Chain Management

Department of Operations, Technology, Events, and Hospitality Management

Faculty of Business and Law | Manchester Metropolitan University


Abstract

In the original Best-Worst Method (BWM), a Decision Maker (DM) (i.e. expert) must provide

with certainty one decision-making criterion as the best and another decision-making criterion

as the worst criterion. In the real-world decision-making process applying the original BWM

dealing with subjective judgements of human beings, it is not always straightforward for DMs

to choose only one criterion as either the best or the worst without any level of hesitancy. In

other words, there might be a set of best and a set of worst criteria instead of just one single

best or worst criterion. In this study, a hybrid application of Spanning Trees Enumeration (STE)

and the BWM as a solution is suggested in order to deal with this type of uncertainty and capture

the hesitancy of DMs. This method by applying STE offers an opportunity for DMs to suggest

more than one best or worst criteria. The reason is that in many cases DMs are unable to choose

only one criterion due to uncertainty, hesitancy or lack of information. The proposed method is

capable to calculate which criteria are actually the best and worst ones based on already

provided pair-wise comparisons by DMs.

In the UK energy supply chain, it has been identified that Natural Disasters (ND), Climate

Change (CC), Industrial Action (IA), Affordability (AF), Political Instability (PI), and

Sabotage/Terrorism (ST) are the most crucial risks. In this study, the objectives are twofold: (1)

to theoretically enhance the BWM, and (2) to practically apply it in the UK energy supply chain

risks prioritisation in order to show the applicability of methodological extension of the BWM

as well as verifying the most critical risk dimensions in the UK energy supply chain.


26

Multi-criteria competence analysis (MCCA): A case study on

crowdsourcing delivery personnel on takeaway platform

Longxiao Li *1,2, Xu Wang 1, Jafar Rezaei 2

1 College of Mechanical Engineering, Chongqing University, Chongqing, China

(E-mail: [email protected], [email protected]) 2 Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The

Netherlands


Keywords: Multi-criteria competence analysis; Multi-criteria decision analysis; Crowdsourcing

delivery; Bayesian Best-Worst Method

Abstract

Competence analysis provides a way to determine whether individuals meet the specified

performance criteria, and there are several frameworks in existing literature: Knowledge,

Skills, Experience and Qualifications (KSEQ) (Kurz & Bartram, 2002), Knowledge, Skills,

Abilities and Other characteristics (KSAOs) (Maurer & Lippstreu, 2008), Knowledge,

Skills and Attitudes (KSA) (Mulder, 2014). Although the proposed frameworks are very

well-grounded in theory, they are more difficult to put into practice. For instance, it is not

evident what a company can do when managers have different views regarding the

importance of different dimensions. Therefore, we develop a multi-criteria competence

analysis (MCCA) as a novel approach to evaluating the competence of personnel. Using the

dimensions as criteria and personnel as alternatives, the competence analysis is formulated

as an MCCA. As a generic framework for evaluating the competence of personnel, the steps

of MCCA are described as follows: (i) Determine the objective of the competence analysis

and define the scope of the problem; (ii) Determine the evaluation criteria for competence

analysis of the personnel through competence analysis frameworks and experts’ opinions;

(iii) Collect competence scores of each individual for all criteria from various data sources;

(iv) Find the optimal weights of all criteria that have been identified for the competence

analysis; (v) Find an overall level of the personnel competence with aggregating the scores.

To illustrate the MCCA approach, a real-world case study is carried out involving a Chinese

takeaway delivery platform. Following the above MCCA steps, we use BWM (Rezaei,

2015) in the case study because of its several attractive features such as providing more

reliable pairwise comparisons, while mitigating possible anchoring bias, most data (and

time) efficient, and also providing a consistency check. There are several extended versions

of BWM and in this paper, we use the Bayesian BWM (Mohammadi & Rezaei, 2019), to

determine the weights of the criteria in MCCA based on the data collected from managers

of the platform company. The Bayesian BWM introduces the concept of credal ranking. In

the proposed main criteria as shown in Figure 1, Skills is the most important competence of

all the main criteria with the confidence level value 1, 1, 0.71 compared with the Traits,

Knowledge and Abilities. Also, it is not surprising to see that “Knowledge” is considered

to be the least important criterion, with even “Traits” ranking higher with a confidence of

0.94. This is in line with the actual situation involving crowdsourcing delivery personnel in

China because, to attract more people to the crowdsourcing delivery platform, the entry

barrier is kept relatively low.


27

Figure 1. Credal ranking for the main criteria

Given the weights and the competence scores for a sample of crowdsourcing delivery

personnel, we use additive value function to identify the overall competence scores, which

reflects the level of competence for their job. On this basis, some statistic results can be

derived, as shown in Table 1.

Table 1. Statistical results for overall competence scores

Personnel N Mean Max Min S.D.

Overall 81 0.575 0.733 0.314 0.089

Table 1 shows that, among all the crowdsourcing delivery personnel, there is a significant

difference between the highest competence score and the lowest score. The same situation

is also reflected in the standard deviation, which is relatively high. It also clearly illustrates

the fact that the competence of 81 crowdsourcing delivery personnel varies significantly. In

addition, we discuss the relationship between the competence level and registration time. A

comparison of four groups’ crowdsourcing delivery personnel shows that their competence

levels improve over time, while more pronounced fluctuations reflect a shorter time on the

job. In our case study, the MCCA approach developed in this paper is validated in the

context of crowdsourcing delivery, it also can be extended and applied to analyze the

competence of personnel in many other industries as well.

Acknowledgment: The authors would like to express appreciation for the support of the China

Scholarship Council and National Key R&D Project [Grant No. 2018YFB1403602]. We would

also like to thank Fang Li and the managers of the takeaway platform for their sincere help.

References:

Kurz, R., & Bartram, D. (2002). Competency and individual performance: Modelling the world

of work. Organisational effectiveness: The role of Psychology. Wiley. Pp227-258.

Maurer, T. J., & Lippstreu, M. (2008). Expert vs. general working sample differences in KSAO

improvability ratings and relationships with measures relevant to occupational and

organizational psychology. Journal of Occupational and Organizational Psychology,

81(4), 813-829.

Mohammadi, M., & Rezaei, J. (2019). Bayesian best-worst method: A probabilistic group

decision making model. Omega, 102075.

Mulder, M. (2014). Conceptions of Professional Competence. In S. Billett, C. Harteis, & H.

Gruber (Eds.), International Handbook of Research in Professional and Practice-based

Learning (pp. 107-137). Dordrecht: Springer.



28

A hybrid failure assessment approach by an FMEA using fuzzy Bayesian

network and fuzzy best-worst method

Muhammet Gul*1, Melih Yucesan2, Erkan Celik3

1, 3 Department of Industrial Engineering, Munzur University, Turkey

(E-mail: [email protected], [email protected]) 2 Department of Mechanical Engineering, Munzur University, Turkey


Keywords: Failure assessment; FMEA; Fuzzy set; Bayesian network; Best-worst method

Abstract

To assess failures, a number of methods are developed and applied. One of these methods

is failure modes and effects analysis (FMEA). It is a well-known and broadly applied failure

assessment tool. Researchers applied FMEA to various fields, from manufacturing to the

service industry. Since the classical FMEA contains some deficiencies, numerous

improvement FMEAs are performed over the originally recommended version. While in the

one hand, it has merged with some multi-attribute decision-making methods and their fuzzy

versions, on the other hand, it is combined with probabilistic (e.g., Bayesian network),

machine learning (e.g. artificial neural network), and sophisticated methods (e.g., Petri net).

In this study, an FMEA approach using fuzzy Bayesian network (FBN) and fuzzy best-worst

method (FBWM) is proposed and applied to assess failures in plastic production. Parameters

of classical FMEA are modified by adding three sub-parameters under the consequence

parameter, which are entirely specific for the plastic production failure assessment. The

main parameters used under FMEA are named as consequence, detection, and occurrence

likelihood. Under the consequence parameter, three sub-parameters are suggested as

follows: (1) The flexibility of the product is not at the desired level, (2) Product color is not

in desired standard, and (3) The strength of the product is not at the desired level. Weights

of these parameters and sub-parameters are determined by FBWM. FBWM has many pluses

against similar methods, like the fuzzy analytic hierarchy process. The classical BWM

method was created by Rezaei (2015) to derive the weights of the criteria with the smaller

number of comparisons and more consistent comparisons. The best criterion is the one

which has the most vital role in making the decision, while the worst criterion has the

opposite role. Furthermore, the BWM does not only derive the weights independently, but

it can be also integrated with other methods. We have combined it with FMEA in this study.

Then a fuzzy rule-based system is constructed by incorporating Bayesian network, as stated

by Wan et al. (2019). Bayesian network determination is modeled by GeNle 2.4 software.

Flow chart of the proposed hybrid approach is given in Figure 1.

The results of the study are strengthened with the experts’ opinions regarding the importance

of failure modes for the final product and the whole system and supported them by experience

feedback in the observed facility. Final risk priority numbers (RPNs) are obtained as in Table

1. On conclusion of the results from Table 8, the failure prioritization of five failure modes is

𝐹𝑀2 ≻ 𝐹𝑀3 ≻ 𝐹𝑀1 ≻ 𝐹𝑀4 ≻ 𝐹𝑀5. So, the failure mode of FM2 with its highest final RPN

score should be taken the great attention. The control measures should be initially taken for this

failure mode. On the other hand, the lowest attention should be provided to the failure mode of

FM5 as it has the lowest final RPN value.

Finally, a comparative analysis with two approaches of traditional FMEA and FBWM-based

FMEA (without a fuzzy rule-based system incorporating Bayesian network) is fulfilled.


29

Also, a sensitivity analysis is performed to observe the final FMEA score changes in

accordance with the change of subjective probability values.

Figure 1. Flow chart of the proposed hybrid approach.

Table 1. Final RPN values of failure modes. Failure mode Final RPN

Failure to send the appropriate quality of raw materials to the Shredder (FM1) 85.07

The raw material in the extruder cannot be adjusted to the appropriate melting temperature

(FM2) 92.89

Failure to adjust the temperature in the second extruder to an appropriate value (FM3) 92.02

Awaiting cooling time of the product in the press (FM4) 55.6

Deformation of press molds (FM5) 17.44

References:


Wan, C., Yan, X., Zhang, D., Qu, Z., & Yang, Z. (2019). An advanced fuzzy Bayesian-based

FMEA approach for assessing maritime supply chain risks. Transportation Research

Part E: Logistics and Transportation Review, 125, 222-240.

DüğmeStep 1: Failure identification DüğmeStep 2:Parameter weighing DüğmeStep 3:RPN calculation

Set up FMEA expert

Prepare failure mode list, construct hierarch of FMEA

Indentify failures with respect to consequence

occurence likelihood and detection

Determine parameters and sub parameters

Determine the best and the worst parameters/sub

parameters

Execute the fuzzy reference comparations for the best and worst parameters/sub

parameters

Determine the optimal fuzzy weights of parameter/sub

parameters

Make defuzzification and calculate consistency

Determination of fuzzy linguistic scale for each

parameters/sub parameters

Determination of rule table

Injecting the rule table into BN by obtaining expert subjective probabilites

regarding parameter/sub parameters

Execute BN in Genle software

Determine probablity values of failure modes

Determine probablity values of failure modes

Apply utility functions

Obtain final RPN values


30

Using Bayesian Best Worst Method to Assess the Airport Resilience

Huai-Wei Lo1, James J.H. Liou1, Chun-Nen Huang3

1Department of Industrial Engineering and Management, National Taipei University of Technology,

Taipei, Taiwan

(E-mail: [email protected]) 2Department of Fire Science, Central Police University, Taoyuan County, Taiwan

(E-mail: [email protected]) 3Department of Fire Science, Central Police University, Taoyuan County, Taiwan

Keywords: Disaster, Critical Infrastructure, Airport Resilience, Bayesian Best Worst Method

Abstract

International airport is one of the most important critical infrastructures for transportation.

Facing unpredictable natural disasters and man-made threats, whether the airport has sufficient

response and resilience has attracted much attention. This study proposes an airport resilience

assessment framework to examine the proactive planning of airport while facing disaster or

threats. Bayesian Best Worst Method (Bayesian BWM), an effective method to determine the

importance weights of the criteria, is applied to evaluate the priorities of the proposed

indicators. The proposed assessment framework is demonstrated by conducting a case study

involving in Taiwan. The results indicate that adequate disaster response plans, proper airside

isolation measures, and sufficient security personnel are the most critical factors for airport risk

management. Some management implications are provided.



31

Identifying and ranking the barriers to organizational productivity of the

railway industry - using the Best-Worst Method

Mahdie Hamedi1, Mohamad Sadeq Abolhasani2, Hamidreza Fallah Lajimi3*, Zahra

Jafari Soruni4 1 MSc student in Production and Operations Management. Faculty of Management. University of

Tehran, Tehran, Iran.

(E-mail: [email protected]) 2 MSc student in Production and Operations Management. Faculty of Management. University of

Tehran, Tehran, Iran.

(E-mail: [email protected]) 3 Assistance Professor, Department of Industrial Management Faculty of Economics and

Administrative Sciences. University of Mazandaran, Babolsar, Iran. Corresponding author.

(E-mail: [email protected]) 4 MSc student in Operation Research. Faculty of Management. University of Tehran, Tehran, Iran. (E-

mail: [email protected])

Keywords: Productivity, Productivity Barriers, Railway Industry, Best-Worst Method

Abstract

Productivity is a concept looking for the improvement of the status quo continuously. The

public service sector provides people with numerous sensory services. Hence, the

productivity of service provider organizations, such as the railway industry, is of paramount

importance. The aim of the current study is to provide a complete and systematic structure

of the barriers to improvement of organizational productivity in the railway industry. For

this purpose, the required criteria have been extracted from previous researches done in the

railway industry. In order to weighting and determination of the identified criteria, after

holding interview sessions with the railway industry experts, the multi criteria decision

making method called best-worst technique (BWM) has been used to rank the criteria. The

acquired result indicates that systematic, legal and political, environmental, occupational ,

organizational and individual obstacles respectively are the most influencing barriers to

productivity. The present study is functional in terms of purpose and descriptive-survey in

terms of data gathering.

Abstract review

Productivity is a mindset that seeks continuous amelioration of the status-quo. Since the

public service sector provides the majority of people with abundant services, productivity

in service organizations such as the railway is of paramount importance. The aim of this

paper is to provide a comprehensive framework for identifying crucial barriers to

organizational productivity improvement in the railway industry.

In order to identify and determine the importance of barriers to improving organizational

productivity in the railway industry, required criteria have been extracted from the literature

review. Afterwards, to calculate the weight and determine the importance of identified

criteria, after interviewing the railway industry experts, using the best - worst method the

criteria were ranked. Afterwards, optima and local weights of barriers calculated using

equation 1(Rezaei, 2016).






32

(1)

Due to the information depicted in table 1, the acquired results indicate that level of facilities

and equipment, workplace environment conditions, organizational and industrial

infrastructure, and economic status in railway service organizations are the most serious

barriers affecting improvement of productivity.

Table 1. Optimal weight of dimensions and criteria Barrier

dimension Dimension

weight Criteria

Criteria

local weight Criteria

local rank Criteria

total weight Criteria

total rank

Individual

barriers 0.0446

Individual mobility 0.2222 2 0.0099 22

Knowledge level 0.1667 3 0.0074 23

Experience 0.5417 1 0.0242 12

Physical status 0.0694 4 0.0031 26

Occupational

barriers 0.1071

Workload 0.1565 2 0.0168 15

Working time 0.1252 4 0.0134 18

Complexity of work 0.1565 2 0.0168 15

Work structure 0.5064 1 0.0543 5

Work uniformity 0.0552 5 0.0059 24

Systematic

barriers 0.4464

Design of railway

system 0.1045 3 0.0467 6

Human-machine

relationships 0.0488 5 0.0218 14

Level of facilities and

equipment 0.4808 1 0.2147 1

workplace

environment

conditions

0.2613 2 0.1167 2

Financial resources 0.1045 3 0.0467 6

Organizational

barriers 0.0893

Job security 0.1250 4 0.0112 21

Leadership 0.4167 1 0.0372 8

Level of trust within

the organization 0.1667 3 0.0149 17

Training programs 0.2500 2 0.0223 13

Changes in

organizational

patterns

0.0417 5 0.0037 25

Legal and

political barriers 0.1786

Policies and strategies 0.1485 3 0.0265 11

Infrastructures 0.6004 1 0.1072 3

pace of industry

growth 0.0655 4 0.0117 20

Sanctions 0.1856 2 0.0331 10

Environmental

barriers 0.1339

Economic status 0.6400 1 0.0857 4

Social conditions 0.2600 2 0.0348 9

Physical conditions of

the workplace 0.1000 3 0.0134 19


33

Due to the results of this study, to overcome the barriers, providing required facilities and

equipment, and preparing appropriate infrastructure must be taken into consideration by the

railway industry so as to improve employee's performance that will ultimately result in

organizational productivity amelioration. To conclude, it is highly recommended that

special attention should be paid to fast-paced changes in technology and railroad

transportation management in order to achieve Iran’s 20-year vision goals.

References

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a

linear model. Omega, 64, 126-130.


34

Identifying and Prioritizing Competency Factors for Platforms

Managing Service Providers in Knowledge-Intensive Crowdsourcing

Context

Biyu Yang*1, Xu Wang2, Zhuofei Ding3

1, 2 School of Mechanical Engineering, Chongqing University, China

(E-mail: [email protected], [email protected]) 3 School of Business and Economics, Chongqing University, China


Keywords: Knowledge-intensive crowdsourcing, Competency, Service provider management,

Topic modelling, BWM

Abstract

Knowledge-intensive crowdsourcing platforms (KICPs) operate as two- or multi-sided

markets, meaning that each side of the market derives externalities from participation of the

respective other side, which is called network effects (Thies et al., 2018). In KIC context,

SPs, usually recognized as an important source of innovation, are varied in backgrounds,

skills, and abilities, making different levels of contributions to crowdsourcing activities. As

more and more service providers (SPs) joining the platform, it is of great challenge for

KICPs to manage and align SPs’ diverse intentions, interests and performance (Boudreau,

2012).

Existing research suggests quality assessment approaches, such as qualification test, gold-

injected method, and iterative quality computation methods, to estimate SPs’ quality and

performance (Dang et al., 2016; Stouthuysen et al., 2018). However, these quality

assessment methods either are task-oriented, or have simple outputs that convey little

insightful information to platforms for management improvement (Li et al., 2019). The

competency theory suggests that competency analysis is an effective approach to

differentiate high from average and low performance based on differences in knowledge,

skills, abilities, or other characteristics (Mirabile, 1997). To address the limitations of

current research, in our research, we introduce competency theory into SPs management in

KIC context, and aim to answer the following questions: (i) what are the competency factors

that can differentiate high-performance SPs from average- and low-performance SPs in KIC

context? (ii) What are the relative importance associated with each of these competency

factors?

To identify and recognize effective competency factors that can differentiate SPs in terms

of their performance, we leveraged quantities of interview records posted online, in which

includes the experiences by successful SPs about what qualities and capabilities SPs should

possess to perform KIC tasks well. We first crawled these online interview posts and

extracted 18 effective competency factors leveraging Latent Dirichlet Allocation (LDA).

Then we mapped the 18 competency factors to and constructed a KSAOs competency model

in KIC environment. To answer the second question, questionnaires were used to collect

experts’ opinion and the Best-Worst Method (BWM) (Rezaei, J., 2015) were applied to

prioritize the competency factors. The global weights of competency factors are presented

in Table 1. According to our results, skill is the most important competency cluster among

the four clusters and communication ability has the highest influence on SPs’ performance.



35

Table 1 Global weights of Sub-competency

Acknowledgment: The authors would like to express appreciation for the support of the

National Science and Technology Support Program of China [Project NO. 2018YFB1403602],

the Technological Innovation and Application Program of Chongqing [Project No.

cstc2018jszx-cyzdX0081].

References:

Dang, D., Liu, Y., Zhang, X., Huang, S., 2016. A Crowdsourcing Worker Quality Evaluation

Algorithm on MapReduce for Big Data Applications, IEEE Transactions on Parallel

and Distributed Systems, 27(7), 1879-1888.

Li, K., Wang, S., Cheng, X., 2019. Crowdsourcee evaluation based on persuasion game,

Computer Networks, 159, 1-9.

Liu, X., Chen, H., 2020. Sharing Economy: Promote Its Potential to Sustainability by

Regulation, Sustainability, 12(3), 1-13.

Mirabile, R.J., 1997. Everything you wanted to know about competency modeling, Training &

Development, 51(8), 73.

Stouthuysen, K., Teunis, I., Reusen, E., Slabbinck, H., 2018. Initial trust and intentions to buy:

The effect of vendor-specific guarantees, customer reviews and the role of online

shopping experience☆, Electronic Commerce Research and Applications, 27, 23-38.

Thies, F., Wessel, M., Benlian, A., 2018. Network effects on crowdfunding platforms:

Exploring the implications of relaxing input control, Information Systems Journal, 28,

1239-1262.

Tiwana, A., 2015. Evolutionary Competition in Platform Ecosystems, Information Systems

Research, 26(2), 266-281.

Rezaei, J., 2015. Best-worst multi-criteria decision-making method, Omega, 53, 49-57.

Main competency Weight Rank Main competency Weight Rank

Communication ability 0.088 1 Branding 0.028 10

Profession experience 0.065 2 Trustworthiness 0.028 11

Entrepreneurial experience 0.050 3 Online and offline

coordination 0.025 12

Customer relationship

management 0.040 4 Professional dedication 0.023 13

Customer acceptance 0.040 5 Reasonable suggestion 0.020 14

Modification and after-sales

service 0.037 6 Team composition 0.018 15

Demand understanding 0.036 7 Competitive spirit 0.014 16

Customers’ industry

background 0.032 8 Achievement orientation 0.012 17

Innovation ability 0.032 9 Team environment 0.011 18


36

An analysis of sustainable business practices: An emerging economy

perspective

Himanshu Gupta*1, Ashwani Kumar2

1 Department of Management Studies IIT (ISM) Dhanbad, India

(E-mail: [email protected]) 2 Jaipuria Institute of Management, Noida, India


Keywords: Industry 4.0; Circular Economy; Sustainable and Cleaner Production; Sustainability

Abstract

In the era of industrial digitalization, the linkage between Industry 4.0 (I4.0) and the circular

economy (CE) persistently emerge more clearly to explore various paths through which the

objectives of ecological sustainability can achieved (Tseng et al. 2018). Circular economy

implies an alternate way to deal with cleaner production strategies. In other words, it goes from

a linear procedure that sees the utilization of raw or virgin materials and the generation of

production waste that is discarded by the companies, to a model that recovers itself, changing

what is normally viewed as waste into an asset (Lieder and Rashid 2016). A recent study by

Ellen MacArther Foundation and the Mckinsey Center for Business and Environment estimate

that consumption of new or virgin material could be reduced by as much as 32% within 15

years and 53% by the end of 2050. New or raw material can be replaced with recovered and

repurposed materials in cascaded use, in circular business model (Lakatos et al., 2018). Industry

4.0 also plays equally critical role to achieve sustainability of the organizations through its

various tools and practices. Industry 4.0 including such a concept like cloud manufacturing

(CM), additive manufacturing (AM) and disruptive technologies such as Big data and analytics

(BDA), cloud computing, artificial intelligence (AI), and internet of things (IoT) are playing a

pivotal role in circular business model (CBM) (Bocken et al., 2016). Considering the

importance of adopting circular business model and achieving sustainability in the

organizations, this study focuses on analyzing the sustainable business practices in Indian

organizations. A total of eighteen sustainable business practices were identified through

literature review and discussion with experts. These were further categorized into three main

categories. Best Worst Method (BWM) developed by Rezaei (2015) is applied on the responses

obtained from ten different experts. The practices and their obtained ranks are depicted in Table

1.

Circular economy related practices emerged are the most important one for achieving circular

business model and sustainability at the organization. Managers should focus on enhancing

supply chain traceability, Supply chain traceability practices helps in sharing of real time

information of about waste generated at each stage of the supply chain and thus helps in waste

minimization and optimum utilization of the resources. Reuse and recycling infrastructure also

needs to be developed, once the useful life of products is over, they are often discarded and are

many times kept in stock yards without any processing on them. Recycling and reuse

infrastructure and facilities if present can help in extraction of useful components and resources

from these products, which can be reused in some other products, thus greatly reducing the

resource burden of organizations and sustainable development of the businesses.


37

Table 1 Criteria weights and rankings for Industry 4.0, SCP and Circular Economy practices

Main Category

Practices

Main Category

Practices

Weights

Sub Category Practices

Criteria

Sub Category

Practices

Weights

Global

Weight

s

Global

Rankin

g

Industry 4.0 (IDY)

0.159

IoT (Internet of Things)

(IDY1) 0.155 0.025 16

Big data technologies (IDY2) 0.259 0.041 12

Smart factory and Cloud

manufacturing (IDY3) 0.154 0.025 17

Additive manufacturing and

3-D printing technologies

(IDY4)

0.356 0.057 6

Robotic systems (IDY5) 0.075 0.012 18

Sustainable and

Cleaner Production

(SCP)

0.337

Top management

commitment (SCP1) 0.109 0.037 15

Energy and material use

(SCP2) 0.110 0.037 14

Natural and clean

environment (SCP3) 0.266 0.090 3

Packaging and design (SCP4) 0.134 0.045 11

Competency and skillset

building of workforce (SCP5) 0.159 0.054 8

Supply chain collaboration

and integration (SCP6) 0.221 0.075 5

Circular Economy

(CEY)

0.504

Reuse and recycling

infrastructure (CEY1) 0.215 0.108 2

End of life determination

(CEY2) 0.079 0.040 13

Supply chain

traceability/information

(CEY3)

0.227 0.114 1

Reduction is supply related

risks (CEY4) 0.104 0.052 10

Legal compliance (CEY5) 0.162 0.081 4

Investment recovery and

long-term profits (CEY6) 0.104 0.052 9

Global standards and

sustainability goals (CEY7) 0.110 0.055 7

References

Bocken, N. M., De Pauw, I., Bakker, C., & van der Grinten, B. (2016). Product design and

business model strategies for a circular economy. Journal of Industrial and Production

Engineering, 33(5), 308-320.

Lakatos, E. S., Cioca, L. I., Dan, V., Ciomos, A. O., Crisan, O. A., & Barsan, G. (2018). Studies

and investigation about the attitude towards sustainable production, consumption and

waste generation in line with circular economy in Romania. Sustainability, 10(3), 865-

890.

Lieder, M., & Rashid, A. (2016). Towards circular economy implementation: a comprehensive

review in context of manufacturing industry. Journal of cleaner production, 115, 36-

51.


Tseng, M. L., Tan, R. R., Chiu, A. S., Chien, C. F., & Kuo, T. C. (2018). Circular economy

meets industry 4.0: can big data drive industrial symbiosis?. Resources, Conservation

and Recycling, 131, 146-147.

THE FIRST INTERNATIONAL WORKSHOP ON

BEST-WORST METHOD

BOOK OF ABSTRACTS 2020

[email protected]

11-12 June 2020

Delft, The Netherlands


BEST-WORST METHOD

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